In this project, we intend to segment natural images by combing colour and texture information. For this we will be using an unsupervised image segmentation framework (referred to as CTex) that is based on the adaptive inclusion of color and texture in the process of data partition. It is new formulation for the extraction of color features that will evaluate the input image in a multispace color representation. To achieve this, we will be using the opponent characteristics of the RGB and YIQ color spaces where the key component will be the inclusion of the Self Organizing Map (SOM) network in the computation of the dominant colors and estimation of the optimal number of clusters in the image. The texture features will be computed using a multichannel texture decomposition scheme based on Gabor filtering.